The present invention relates generally to the field of digital imaging and particularly to enhancement of volumetric images. More particularly, the invention relates to techniques for processing images in a combination of two-dimensional and three-dimensional analysis steps.
Digital images are typically made and processed in either two or three dimensions. Two-dimensional (2D) images are conventionally made by impacting a digital detector with radiation. The detector may be a charge coupled device, as in digital cameras, or a more complex detector such as those used in digital X-ray and various radiographic techniques. Three-dimensional (3D) image data may be acquired as a plurality of 2D datasets. Techniques for acquiring 3D images include magnetic resonance imaging (MRI), computer tomography (CT) imaging systems, and so forth.
Techniques have also been developed for enhancing 2D and 3D images. In general, these techniques are specifically adapted to either 2D image enhancement or 3D image enhancement. Assumptions made in either technique may generally hold valid for specific situations only, however. For example, for images having pixels of a first pitch or special resolution (i.e., the number of pixels per unit length or area), in both an image plane and in a third dimension, existing techniques may perform adequately. However, where the dimensions are different, information may be lost or analysis of the content of the images may be distorted. This is particularly true of images having a greater depth in a direction orthogonal to an image plane. Such images may be termed “thick slice” or “thick volumetric” images.
Several 2D and 3D image enhancement frameworks have been proposed for enhancing 2D and 3D images. In general, such enhancement techniques are useful for identifying features and objects of interest, typically visible objects in the images. Depending upon the context, such features may be circumscribed, identified, categorized, and analyzed, such as for recognition purposes. In a medical diagnostic context, for example, various anatomies and disease states may be determined based upon such image analysis. The analysis may similarly be use for visualization of structures and anatomies. In other contexts, such as part inspection, defects and internal features may be visualized and analyzed in a similar manner. Still further, in contexts such as baggage and parcel inspection, the internal contents of objects may be determined by analysis and recognition techniques.
Thick volumetric images have characteristics of both 2D and 3D images. For such images, 2D filtering does not make full use of a third dimension. That is, sampling in a third dimension, which may be referred to as a “Z direction”, may be rather poor in such image data, resulting in relatively poor results of the analysis when using 3D filtering.
There is a need, therefore, for improved techniques for analyzing thick volumetric images. There is a particular need for a technique that can make use of additional information provided by volumetric data while still maintaining full use of in-plane data. Such techniques could enhance details by highlighting areas of visual interest better than 2D techniques.